Evidently AI

Catch hallucinations, safety risks, and quality issues in LLM products before they impact users. Automate, customize, and track AI testing at scale.

The page offers a video course with 10 hands-on code tutorials covering custom LLM judges, RAG evaluations, and adversarial testing.

ML evaluation, testing, and monitoring platform. Track data quality, drift, and performance. Classification, regression, ranking, and recommender models.

The page lists 45 internal AI and ML platforms, compiled from more than 150 blog posts and conference talks.

The guide covers key concepts for operating production ML models, including how to maintain them after deployment and what to prepare.

The guide covers ranking metrics for evaluating the quality of ranking and recommender systems.

Insights on building AI products, AI quality and MLOps.

The collection includes open-source tutorials on AI and ML observability, LLM evaluation, and MLOps, with end-to-end code examples.

Explore Evidently AI blog posts on machine learning in production by Dasha Maliugina.

The page features blog posts by Dayle Fernandes on machine learning in production.

Explore Evidently AI blog posts on machine learning in production by Elena Samuylova.

Explore Evidently AI blog posts on machine learning in production by Emeli Dral.

Fill out the form to connect with the team behind Evidently.

The open-source offering tests LLMs and monitors performance across AI applications, RAG systems, and multi-agent workflows.

The company's Privacy Policy explains how it collects, safeguards, and discloses information resulting from use of its services.

The agreement governs access to and use of the Cloud Services operated by Evidently AI.

The Terms of Service govern use of the services operated by Evidently AI.

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